Identification of Multiview Gene Modules Using Mutual Information-Based Hypograph Mining

Detection of gene-modules is one of the fundamental tasks for the integral analysis of network architecture. In this paper, we propose a novel algorithm using an integrated approach comprising statistical method and normalized mutual information-based hypograph mining for discovering the multiview co-similarity gene modules contained in multiview datasets. For this purpose, we first identify the statistically significant genes corresponding to each data profile and subsequently obtain the union set consisting of all these statistically significant genes. For each data profile, we then propose a new similarity score called as integrated normalized mutual information to obtain the similarity scores across all possible pairs of genes belonging to the union set by employing the results of gene clustering obtained through applying normalized mutual information-based graph clustering on the corresponding data profile. Moreover, we propose a new information theoretic measure called as multiview normalized mutual information to integrate all the similarity scores of a given gene-pair obtained across all the data profiles. For the experiment, we utilize one of the recently used multiview dataset named TCGA acute myeloid leukemia dataset comprising five different categories of data profiles. Furthermore, the co-similarity strengths of all the multiview gene modules obtained using the proposed method (PM) are reported. Finally, we provide a comparative study between the proposed and other existing methods for demonstrating the superiority of the PM over others. Code is available in http://ieeexplore.ieee.org.